HCSL Publications

Bayesian Medical Diagnosis

1. Chatzimichail T., Hatjimihail AT. A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis, Diagnostics 2023, 13(19), 3135.

DOI: 10.3390/diagnostics13193135. PMCID: PMC10572594. PMID: 37835877.

Abstract

Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a failure to capture the intricate relations between diagnostic tests and the varying prevalence of diseases. To explore this further, we have developed a freely available specialized computational tool that employs Bayesian inference to calculate the posterior probability of disease diagnosis. This novel software comprises of three distinct modules, each designed to allow users to define and compare parametric and nonparametric distributions effectively. The tool is equipped to analyze datasets generated from two separate diagnostic tests, each performed on both diseased and nondiseased populations. We demonstrate the utility of this software by analyzing fasting plasma glucose and glycated hemoglobin A1c data from the National Health and Nutrition Examination Survey. Our results are validated using the oral glucose tolerance test as a reference standard, and we explore both parametric and nonparametric distribution models for the Bayesian diagnosis of diabetes mellitus.

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Full text in Diagnostics

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2. Chatzimichail T, Hatjimihail AT. A Software Tool for Estimating Uncertainty of Bayesian Posterior Probability for Disease. Diagnostics 2024, 14(4), 402.

DOI: 10.3390/diagnostics14040402. PMCID: PMC10887534. PMID: 38396440.

Abstract

The role of medical diagnosis is essential in patient care and healthcare. Established diagnostic practices typically rely on predetermined clinical criteria and numerical thresholds. In contrast, Bayesian inference provides an advanced framework that supports diagnosis via in-depth probabilistic analysis. This study’s aim is to introduce a software tool dedicated to the quantification of uncertainty in Bayesian diagnosis, a field that has seen minimal exploration to date. The presented tool, a freely available specialized software program, utilizes uncertainty propagation techniques to estimate the sampling, measurement, and combined uncertainty of the posterior probability for disease. It features two primary modules and fifteen submodules, all designed to facilitate the estimation and graphical representation of the standard uncertainty of the posterior probability estimates for diseased and non-diseased population samples, incorporating parameters such as the mean and standard deviation of the test measurand, the size of the samples, and the standard measurement uncertainty inherent in screening and diagnostic tests. Our study showcases the practical applica-tion of the program by examining the fasting plasma glucose data sourced from the National Health and Nutrition Examination Survey. Parametric distribution models are explored to assess the uncertainty of Bayesian posterior probability for diabetes mellitus, using the oral glucose tolerance test as the reference diagnostic method.

Snapshot of the program

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Full text in Diagnostics

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3. Chatzimichail T, Hatjimihail AT. A Software Tool for Bayesian Probabilistic Methods in Medical Diagnostics. Submitted for Publication, 2024.

Abstract

Background: In medical diagnostics, determining disease probabilities and understanding associated uncertainty and confidence intervals is essential for patient care.

Objective: This study introduces a software tool developed in the Wolfram Language for the parametric estimation, visualization, and comparison of Bayesian diagnostic measures and their uncertainty.

Methods: The tool employs Bayes' theorem to compute posterior probability for disease and absence of the disease, and diagnostic thresholds derived positive and negative predictive value. It also quantifies their standard sampling, measurement, and combined uncertainty using normal, lognormal, and gamma distributions and applying uncertainty propagation methods.

Results: : The tool generates diagnostic measures, standard uncertainty, and confidence intervals estimates and provides plots that offer insights into their precision, thereby supporting clinical decision-making. A case study analyzing fasting plasma glucose data from the National Health and Nutrition Examination Survey in the U.S.A., demonstrates the tool's utility for diagnosing diabetes mellitus. The results underscore the influence of measurement uncertainty on the Bayesian diagnostic measures.

Conclusion: : The tool generates diagnostic measures, standard uncertainty, and confidence intervals estimates and provides plots that offer insights into their precision, thereby supporting clinical decision-making. A case study analyzing fasting plasma glucose data from the National Health and Nutrition Examination Survey in the U.S.A., demonstrates the tool's utility for diagnosing diabetes mellitus. The results underscore the influence of measurement uncertainty on the Bayesian diagnostic measures.

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